Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Wednesday 17 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 148,423 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 148,413 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 30
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 7
## 105 2020-06-13 East of England 2
## 106 2020-06-14 East of England 2
## 107 2020-06-15 East of England 2
## 108 2020-03-01 London 0
## 109 2020-03-02 London 0
## 110 2020-03-03 London 0
## 111 2020-03-04 London 0
## 112 2020-03-05 London 0
## 113 2020-03-06 London 1
## 114 2020-03-07 London 1
## 115 2020-03-08 London 0
## 116 2020-03-09 London 1
## 117 2020-03-10 London 0
## 118 2020-03-11 London 7
## 119 2020-03-12 London 6
## 120 2020-03-13 London 10
## 121 2020-03-14 London 14
## 122 2020-03-15 London 10
## 123 2020-03-16 London 18
## 124 2020-03-17 London 25
## 125 2020-03-18 London 31
## 126 2020-03-19 London 25
## 127 2020-03-20 London 44
## 128 2020-03-21 London 50
## 129 2020-03-22 London 54
## 130 2020-03-23 London 64
## 131 2020-03-24 London 87
## 132 2020-03-25 London 113
## 133 2020-03-26 London 130
## 134 2020-03-27 London 130
## 135 2020-03-28 London 122
## 136 2020-03-29 London 147
## 137 2020-03-30 London 150
## 138 2020-03-31 London 181
## 139 2020-04-01 London 202
## 140 2020-04-02 London 190
## 141 2020-04-03 London 196
## 142 2020-04-04 London 230
## 143 2020-04-05 London 195
## 144 2020-04-06 London 197
## 145 2020-04-07 London 220
## 146 2020-04-08 London 238
## 147 2020-04-09 London 206
## 148 2020-04-10 London 170
## 149 2020-04-11 London 177
## 150 2020-04-12 London 158
## 151 2020-04-13 London 166
## 152 2020-04-14 London 144
## 153 2020-04-15 London 142
## 154 2020-04-16 London 139
## 155 2020-04-17 London 100
## 156 2020-04-18 London 101
## 157 2020-04-19 London 103
## 158 2020-04-20 London 95
## 159 2020-04-21 London 95
## 160 2020-04-22 London 109
## 161 2020-04-23 London 77
## 162 2020-04-24 London 71
## 163 2020-04-25 London 58
## 164 2020-04-26 London 53
## 165 2020-04-27 London 51
## 166 2020-04-28 London 43
## 167 2020-04-29 London 44
## 168 2020-04-30 London 40
## 169 2020-05-01 London 41
## 170 2020-05-02 London 40
## 171 2020-05-03 London 36
## 172 2020-05-04 London 30
## 173 2020-05-05 London 25
## 174 2020-05-06 London 37
## 175 2020-05-07 London 37
## 176 2020-05-08 London 29
## 177 2020-05-09 London 23
## 178 2020-05-10 London 26
## 179 2020-05-11 London 18
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## 182 2020-05-14 London 20
## 183 2020-05-15 London 18
## 184 2020-05-16 London 14
## 185 2020-05-17 London 15
## 186 2020-05-18 London 9
## 187 2020-05-19 London 13
## 188 2020-05-20 London 19
## 189 2020-05-21 London 12
## 190 2020-05-22 London 10
## 191 2020-05-23 London 6
## 192 2020-05-24 London 7
## 193 2020-05-25 London 9
## 194 2020-05-26 London 12
## 195 2020-05-27 London 7
## 196 2020-05-28 London 8
## 197 2020-05-29 London 7
## 198 2020-05-30 London 12
## 199 2020-05-31 London 6
## 200 2020-06-01 London 10
## 201 2020-06-02 London 7
## 202 2020-06-03 London 6
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## 205 2020-06-06 London 0
## 206 2020-06-07 London 4
## 207 2020-06-08 London 5
## 208 2020-06-09 London 2
## 209 2020-06-10 London 7
## 210 2020-06-11 London 5
## 211 2020-06-12 London 2
## 212 2020-06-13 London 3
## 213 2020-06-14 London 2
## 214 2020-06-15 London 0
## 215 2020-03-01 Midlands 0
## 216 2020-03-02 Midlands 0
## 217 2020-03-03 Midlands 1
## 218 2020-03-04 Midlands 0
## 219 2020-03-05 Midlands 0
## 220 2020-03-06 Midlands 0
## 221 2020-03-07 Midlands 0
## 222 2020-03-08 Midlands 3
## 223 2020-03-09 Midlands 1
## 224 2020-03-10 Midlands 0
## 225 2020-03-11 Midlands 2
## 226 2020-03-12 Midlands 6
## 227 2020-03-13 Midlands 5
## 228 2020-03-14 Midlands 4
## 229 2020-03-15 Midlands 5
## 230 2020-03-16 Midlands 11
## 231 2020-03-17 Midlands 8
## 232 2020-03-18 Midlands 13
## 233 2020-03-19 Midlands 8
## 234 2020-03-20 Midlands 28
## 235 2020-03-21 Midlands 13
## 236 2020-03-22 Midlands 31
## 237 2020-03-23 Midlands 33
## 238 2020-03-24 Midlands 41
## 239 2020-03-25 Midlands 48
## 240 2020-03-26 Midlands 64
## 241 2020-03-27 Midlands 72
## 242 2020-03-28 Midlands 89
## 243 2020-03-29 Midlands 92
## 244 2020-03-30 Midlands 90
## 245 2020-03-31 Midlands 123
## 246 2020-04-01 Midlands 140
## 247 2020-04-02 Midlands 142
## 248 2020-04-03 Midlands 124
## 249 2020-04-04 Midlands 151
## 250 2020-04-05 Midlands 164
## 251 2020-04-06 Midlands 140
## 252 2020-04-07 Midlands 123
## 253 2020-04-08 Midlands 186
## 254 2020-04-09 Midlands 139
## 255 2020-04-10 Midlands 127
## 256 2020-04-11 Midlands 142
## 257 2020-04-12 Midlands 139
## 258 2020-04-13 Midlands 120
## 259 2020-04-14 Midlands 116
## 260 2020-04-15 Midlands 147
## 261 2020-04-16 Midlands 102
## 262 2020-04-17 Midlands 118
## 263 2020-04-18 Midlands 115
## 264 2020-04-19 Midlands 92
## 265 2020-04-20 Midlands 107
## 266 2020-04-21 Midlands 86
## 267 2020-04-22 Midlands 78
## 268 2020-04-23 Midlands 103
## 269 2020-04-24 Midlands 79
## 270 2020-04-25 Midlands 72
## 271 2020-04-26 Midlands 81
## 272 2020-04-27 Midlands 74
## 273 2020-04-28 Midlands 68
## 274 2020-04-29 Midlands 53
## 275 2020-04-30 Midlands 56
## 276 2020-05-01 Midlands 64
## 277 2020-05-02 Midlands 51
## 278 2020-05-03 Midlands 52
## 279 2020-05-04 Midlands 61
## 280 2020-05-05 Midlands 58
## 281 2020-05-06 Midlands 59
## 282 2020-05-07 Midlands 48
## 283 2020-05-08 Midlands 34
## 284 2020-05-09 Midlands 37
## 285 2020-05-10 Midlands 42
## 286 2020-05-11 Midlands 33
## 287 2020-05-12 Midlands 45
## 288 2020-05-13 Midlands 40
## 289 2020-05-14 Midlands 37
## 290 2020-05-15 Midlands 40
## 291 2020-05-16 Midlands 34
## 292 2020-05-17 Midlands 31
## 293 2020-05-18 Midlands 34
## 294 2020-05-19 Midlands 34
## 295 2020-05-20 Midlands 36
## 296 2020-05-21 Midlands 32
## 297 2020-05-22 Midlands 27
## 298 2020-05-23 Midlands 34
## 299 2020-05-24 Midlands 19
## 300 2020-05-25 Midlands 26
## 301 2020-05-26 Midlands 33
## 302 2020-05-27 Midlands 29
## 303 2020-05-28 Midlands 27
## 304 2020-05-29 Midlands 20
## 305 2020-05-30 Midlands 20
## 306 2020-05-31 Midlands 21
## 307 2020-06-01 Midlands 20
## 308 2020-06-02 Midlands 22
## 309 2020-06-03 Midlands 23
## 310 2020-06-04 Midlands 15
## 311 2020-06-05 Midlands 21
## 312 2020-06-06 Midlands 20
## 313 2020-06-07 Midlands 16
## 314 2020-06-08 Midlands 15
## 315 2020-06-09 Midlands 17
## 316 2020-06-10 Midlands 14
## 317 2020-06-11 Midlands 13
## 318 2020-06-12 Midlands 9
## 319 2020-06-13 Midlands 3
## 320 2020-06-14 Midlands 12
## 321 2020-06-15 Midlands 1
## 322 2020-03-01 North East and Yorkshire 0
## 323 2020-03-02 North East and Yorkshire 0
## 324 2020-03-03 North East and Yorkshire 0
## 325 2020-03-04 North East and Yorkshire 0
## 326 2020-03-05 North East and Yorkshire 0
## 327 2020-03-06 North East and Yorkshire 0
## 328 2020-03-07 North East and Yorkshire 0
## 329 2020-03-08 North East and Yorkshire 0
## 330 2020-03-09 North East and Yorkshire 0
## 331 2020-03-10 North East and Yorkshire 0
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## 334 2020-03-13 North East and Yorkshire 0
## 335 2020-03-14 North East and Yorkshire 0
## 336 2020-03-15 North East and Yorkshire 2
## 337 2020-03-16 North East and Yorkshire 3
## 338 2020-03-17 North East and Yorkshire 1
## 339 2020-03-18 North East and Yorkshire 2
## 340 2020-03-19 North East and Yorkshire 6
## 341 2020-03-20 North East and Yorkshire 5
## 342 2020-03-21 North East and Yorkshire 6
## 343 2020-03-22 North East and Yorkshire 7
## 344 2020-03-23 North East and Yorkshire 9
## 345 2020-03-24 North East and Yorkshire 8
## 346 2020-03-25 North East and Yorkshire 18
## 347 2020-03-26 North East and Yorkshire 21
## 348 2020-03-27 North East and Yorkshire 28
## 349 2020-03-28 North East and Yorkshire 35
## 350 2020-03-29 North East and Yorkshire 38
## 351 2020-03-30 North East and Yorkshire 64
## 352 2020-03-31 North East and Yorkshire 60
## 353 2020-04-01 North East and Yorkshire 67
## 354 2020-04-02 North East and Yorkshire 74
## 355 2020-04-03 North East and Yorkshire 100
## 356 2020-04-04 North East and Yorkshire 105
## 357 2020-04-05 North East and Yorkshire 92
## 358 2020-04-06 North East and Yorkshire 96
## 359 2020-04-07 North East and Yorkshire 102
## 360 2020-04-08 North East and Yorkshire 107
## 361 2020-04-09 North East and Yorkshire 111
## 362 2020-04-10 North East and Yorkshire 117
## 363 2020-04-11 North East and Yorkshire 98
## 364 2020-04-12 North East and Yorkshire 84
## 365 2020-04-13 North East and Yorkshire 94
## 366 2020-04-14 North East and Yorkshire 107
## 367 2020-04-15 North East and Yorkshire 96
## 368 2020-04-16 North East and Yorkshire 103
## 369 2020-04-17 North East and Yorkshire 88
## 370 2020-04-18 North East and Yorkshire 95
## 371 2020-04-19 North East and Yorkshire 88
## 372 2020-04-20 North East and Yorkshire 100
## 373 2020-04-21 North East and Yorkshire 76
## 374 2020-04-22 North East and Yorkshire 84
## 375 2020-04-23 North East and Yorkshire 63
## 376 2020-04-24 North East and Yorkshire 72
## 377 2020-04-25 North East and Yorkshire 69
## 378 2020-04-26 North East and Yorkshire 65
## 379 2020-04-27 North East and Yorkshire 65
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## 381 2020-04-29 North East and Yorkshire 69
## 382 2020-04-30 North East and Yorkshire 57
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## 384 2020-05-02 North East and Yorkshire 48
## 385 2020-05-03 North East and Yorkshire 40
## 386 2020-05-04 North East and Yorkshire 49
## 387 2020-05-05 North East and Yorkshire 40
## 388 2020-05-06 North East and Yorkshire 50
## 389 2020-05-07 North East and Yorkshire 45
## 390 2020-05-08 North East and Yorkshire 42
## 391 2020-05-09 North East and Yorkshire 44
## 392 2020-05-10 North East and Yorkshire 40
## 393 2020-05-11 North East and Yorkshire 29
## 394 2020-05-12 North East and Yorkshire 27
## 395 2020-05-13 North East and Yorkshire 28
## 396 2020-05-14 North East and Yorkshire 30
## 397 2020-05-15 North East and Yorkshire 32
## 398 2020-05-16 North East and Yorkshire 35
## 399 2020-05-17 North East and Yorkshire 26
## 400 2020-05-18 North East and Yorkshire 29
## 401 2020-05-19 North East and Yorkshire 27
## 402 2020-05-20 North East and Yorkshire 21
## 403 2020-05-21 North East and Yorkshire 33
## 404 2020-05-22 North East and Yorkshire 22
## 405 2020-05-23 North East and Yorkshire 18
## 406 2020-05-24 North East and Yorkshire 25
## 407 2020-05-25 North East and Yorkshire 21
## 408 2020-05-26 North East and Yorkshire 21
## 409 2020-05-27 North East and Yorkshire 22
## 410 2020-05-28 North East and Yorkshire 20
## 411 2020-05-29 North East and Yorkshire 25
## 412 2020-05-30 North East and Yorkshire 20
## 413 2020-05-31 North East and Yorkshire 19
## 414 2020-06-01 North East and Yorkshire 16
## 415 2020-06-02 North East and Yorkshire 22
## 416 2020-06-03 North East and Yorkshire 22
## 417 2020-06-04 North East and Yorkshire 17
## 418 2020-06-05 North East and Yorkshire 17
## 419 2020-06-06 North East and Yorkshire 20
## 420 2020-06-07 North East and Yorkshire 13
## 421 2020-06-08 North East and Yorkshire 11
## 422 2020-06-09 North East and Yorkshire 11
## 423 2020-06-10 North East and Yorkshire 16
## 424 2020-06-11 North East and Yorkshire 6
## 425 2020-06-12 North East and Yorkshire 8
## 426 2020-06-13 North East and Yorkshire 8
## 427 2020-06-14 North East and Yorkshire 11
## 428 2020-06-15 North East and Yorkshire 5
## 429 2020-03-01 North West 0
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## 445 2020-03-17 North West 4
## 446 2020-03-18 North West 6
## 447 2020-03-19 North West 7
## 448 2020-03-20 North West 10
## 449 2020-03-21 North West 11
## 450 2020-03-22 North West 13
## 451 2020-03-23 North West 16
## 452 2020-03-24 North West 21
## 453 2020-03-25 North West 21
## 454 2020-03-26 North West 29
## 455 2020-03-27 North West 35
## 456 2020-03-28 North West 28
## 457 2020-03-29 North West 46
## 458 2020-03-30 North West 67
## 459 2020-03-31 North West 52
## 460 2020-04-01 North West 86
## 461 2020-04-02 North West 96
## 462 2020-04-03 North West 95
## 463 2020-04-04 North West 98
## 464 2020-04-05 North West 102
## 465 2020-04-06 North West 100
## 466 2020-04-07 North West 134
## 467 2020-04-08 North West 127
## 468 2020-04-09 North West 119
## 469 2020-04-10 North West 117
## 470 2020-04-11 North West 139
## 471 2020-04-12 North West 126
## 472 2020-04-13 North West 129
## 473 2020-04-14 North West 131
## 474 2020-04-15 North West 114
## 475 2020-04-16 North West 134
## 476 2020-04-17 North West 98
## 477 2020-04-18 North West 113
## 478 2020-04-19 North West 71
## 479 2020-04-20 North West 83
## 480 2020-04-21 North West 76
## 481 2020-04-22 North West 86
## 482 2020-04-23 North West 85
## 483 2020-04-24 North West 66
## 484 2020-04-25 North West 65
## 485 2020-04-26 North West 55
## 486 2020-04-27 North West 54
## 487 2020-04-28 North West 57
## 488 2020-04-29 North West 62
## 489 2020-04-30 North West 59
## 490 2020-05-01 North West 45
## 491 2020-05-02 North West 56
## 492 2020-05-03 North West 55
## 493 2020-05-04 North West 48
## 494 2020-05-05 North West 48
## 495 2020-05-06 North West 44
## 496 2020-05-07 North West 49
## 497 2020-05-08 North West 42
## 498 2020-05-09 North West 30
## 499 2020-05-10 North West 41
## 500 2020-05-11 North West 34
## 501 2020-05-12 North West 38
## 502 2020-05-13 North West 25
## 503 2020-05-14 North West 26
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## 505 2020-05-16 North West 32
## 506 2020-05-17 North West 24
## 507 2020-05-18 North West 31
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## 509 2020-05-20 North West 27
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## 514 2020-05-25 North West 31
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## 520 2020-05-31 North West 13
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## 522 2020-06-02 North West 27
## 523 2020-06-03 North West 21
## 524 2020-06-04 North West 20
## 525 2020-06-05 North West 15
## 526 2020-06-06 North West 23
## 527 2020-06-07 North West 17
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## 529 2020-06-09 North West 15
## 530 2020-06-10 North West 13
## 531 2020-06-11 North West 14
## 532 2020-06-12 North West 5
## 533 2020-06-13 North West 6
## 534 2020-06-14 North West 11
## 535 2020-06-15 North West 3
## 536 2020-03-01 South East 0
## 537 2020-03-02 South East 0
## 538 2020-03-03 South East 1
## 539 2020-03-04 South East 0
## 540 2020-03-05 South East 1
## 541 2020-03-06 South East 0
## 542 2020-03-07 South East 0
## 543 2020-03-08 South East 1
## 544 2020-03-09 South East 1
## 545 2020-03-10 South East 1
## 546 2020-03-11 South East 1
## 547 2020-03-12 South East 0
## 548 2020-03-13 South East 1
## 549 2020-03-14 South East 1
## 550 2020-03-15 South East 5
## 551 2020-03-16 South East 8
## 552 2020-03-17 South East 7
## 553 2020-03-18 South East 10
## 554 2020-03-19 South East 9
## 555 2020-03-20 South East 14
## 556 2020-03-21 South East 7
## 557 2020-03-22 South East 25
## 558 2020-03-23 South East 20
## 559 2020-03-24 South East 22
## 560 2020-03-25 South East 29
## 561 2020-03-26 South East 35
## 562 2020-03-27 South East 34
## 563 2020-03-28 South East 36
## 564 2020-03-29 South East 54
## 565 2020-03-30 South East 58
## 566 2020-03-31 South East 65
## 567 2020-04-01 South East 66
## 568 2020-04-02 South East 55
## 569 2020-04-03 South East 72
## 570 2020-04-04 South East 80
## 571 2020-04-05 South East 82
## 572 2020-04-06 South East 88
## 573 2020-04-07 South East 100
## 574 2020-04-08 South East 83
## 575 2020-04-09 South East 104
## 576 2020-04-10 South East 88
## 577 2020-04-11 South East 88
## 578 2020-04-12 South East 88
## 579 2020-04-13 South East 84
## 580 2020-04-14 South East 65
## 581 2020-04-15 South East 72
## 582 2020-04-16 South East 56
## 583 2020-04-17 South East 86
## 584 2020-04-18 South East 57
## 585 2020-04-19 South East 70
## 586 2020-04-20 South East 86
## 587 2020-04-21 South East 50
## 588 2020-04-22 South East 54
## 589 2020-04-23 South East 57
## 590 2020-04-24 South East 64
## 591 2020-04-25 South East 51
## 592 2020-04-26 South East 51
## 593 2020-04-27 South East 40
## 594 2020-04-28 South East 40
## 595 2020-04-29 South East 47
## 596 2020-04-30 South East 29
## 597 2020-05-01 South East 37
## 598 2020-05-02 South East 36
## 599 2020-05-03 South East 17
## 600 2020-05-04 South East 35
## 601 2020-05-05 South East 29
## 602 2020-05-06 South East 25
## 603 2020-05-07 South East 27
## 604 2020-05-08 South East 26
## 605 2020-05-09 South East 28
## 606 2020-05-10 South East 19
## 607 2020-05-11 South East 25
## 608 2020-05-12 South East 27
## 609 2020-05-13 South East 18
## 610 2020-05-14 South East 32
## 611 2020-05-15 South East 24
## 612 2020-05-16 South East 22
## 613 2020-05-17 South East 18
## 614 2020-05-18 South East 22
## 615 2020-05-19 South East 12
## 616 2020-05-20 South East 22
## 617 2020-05-21 South East 14
## 618 2020-05-22 South East 17
## 619 2020-05-23 South East 21
## 620 2020-05-24 South East 16
## 621 2020-05-25 South East 13
## 622 2020-05-26 South East 19
## 623 2020-05-27 South East 17
## 624 2020-05-28 South East 12
## 625 2020-05-29 South East 18
## 626 2020-05-30 South East 8
## 627 2020-05-31 South East 10
## 628 2020-06-01 South East 11
## 629 2020-06-02 South East 12
## 630 2020-06-03 South East 17
## 631 2020-06-04 South East 11
## 632 2020-06-05 South East 9
## 633 2020-06-06 South East 9
## 634 2020-06-07 South East 11
## 635 2020-06-08 South East 5
## 636 2020-06-09 South East 9
## 637 2020-06-10 South East 8
## 638 2020-06-11 South East 3
## 639 2020-06-12 South East 5
## 640 2020-06-13 South East 2
## 641 2020-06-14 South East 5
## 642 2020-06-15 South East 0
## 643 2020-03-01 South West 0
## 644 2020-03-02 South West 0
## 645 2020-03-03 South West 0
## 646 2020-03-04 South West 0
## 647 2020-03-05 South West 0
## 648 2020-03-06 South West 0
## 649 2020-03-07 South West 0
## 650 2020-03-08 South West 0
## 651 2020-03-09 South West 0
## 652 2020-03-10 South West 0
## 653 2020-03-11 South West 1
## 654 2020-03-12 South West 0
## 655 2020-03-13 South West 0
## 656 2020-03-14 South West 1
## 657 2020-03-15 South West 0
## 658 2020-03-16 South West 0
## 659 2020-03-17 South West 2
## 660 2020-03-18 South West 2
## 661 2020-03-19 South West 5
## 662 2020-03-20 South West 3
## 663 2020-03-21 South West 6
## 664 2020-03-22 South West 9
## 665 2020-03-23 South West 9
## 666 2020-03-24 South West 7
## 667 2020-03-25 South West 9
## 668 2020-03-26 South West 11
## 669 2020-03-27 South West 13
## 670 2020-03-28 South West 21
## 671 2020-03-29 South West 18
## 672 2020-03-30 South West 23
## 673 2020-03-31 South West 23
## 674 2020-04-01 South West 22
## 675 2020-04-02 South West 23
## 676 2020-04-03 South West 30
## 677 2020-04-04 South West 42
## 678 2020-04-05 South West 32
## 679 2020-04-06 South West 34
## 680 2020-04-07 South West 39
## 681 2020-04-08 South West 47
## 682 2020-04-09 South West 24
## 683 2020-04-10 South West 46
## 684 2020-04-11 South West 43
## 685 2020-04-12 South West 23
## 686 2020-04-13 South West 27
## 687 2020-04-14 South West 24
## 688 2020-04-15 South West 32
## 689 2020-04-16 South West 29
## 690 2020-04-17 South West 33
## 691 2020-04-18 South West 25
## 692 2020-04-19 South West 31
## 693 2020-04-20 South West 26
## 694 2020-04-21 South West 26
## 695 2020-04-22 South West 23
## 696 2020-04-23 South West 17
## 697 2020-04-24 South West 19
## 698 2020-04-25 South West 15
## 699 2020-04-26 South West 27
## 700 2020-04-27 South West 13
## 701 2020-04-28 South West 17
## 702 2020-04-29 South West 15
## 703 2020-04-30 South West 26
## 704 2020-05-01 South West 6
## 705 2020-05-02 South West 7
## 706 2020-05-03 South West 10
## 707 2020-05-04 South West 17
## 708 2020-05-05 South West 14
## 709 2020-05-06 South West 19
## 710 2020-05-07 South West 16
## 711 2020-05-08 South West 6
## 712 2020-05-09 South West 11
## 713 2020-05-10 South West 5
## 714 2020-05-11 South West 8
## 715 2020-05-12 South West 7
## 716 2020-05-13 South West 7
## 717 2020-05-14 South West 6
## 718 2020-05-15 South West 4
## 719 2020-05-16 South West 4
## 720 2020-05-17 South West 6
## 721 2020-05-18 South West 4
## 722 2020-05-19 South West 6
## 723 2020-05-20 South West 1
## 724 2020-05-21 South West 9
## 725 2020-05-22 South West 6
## 726 2020-05-23 South West 6
## 727 2020-05-24 South West 3
## 728 2020-05-25 South West 8
## 729 2020-05-26 South West 11
## 730 2020-05-27 South West 5
## 731 2020-05-28 South West 9
## 732 2020-05-29 South West 6
## 733 2020-05-30 South West 3
## 734 2020-05-31 South West 2
## 735 2020-06-01 South West 7
## 736 2020-06-02 South West 2
## 737 2020-06-03 South West 5
## 738 2020-06-04 South West 2
## 739 2020-06-05 South West 2
## 740 2020-06-06 South West 1
## 741 2020-06-07 South West 3
## 742 2020-06-08 South West 3
## 743 2020-06-09 South West 0
## 744 2020-06-10 South West 0
## 745 2020-06-11 South West 2
## 746 2020-06-12 South West 2
## 747 2020-06-13 South West 2
## 748 2020-06-14 South West 0
## 749 2020-06-15 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 16 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.9923 -2.2122 -0.2727 2.6380 4.5723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.989e+00 5.254e-02 94.96 <2e-16 ***
## note_lag 1.128e-05 5.192e-07 21.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.13148)
##
## Null deviance: 5149.21 on 45 degrees of freedom
## Residual deviance: 460.06 on 44 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 146.777192 1.000011
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 132.26942 162.520656
## note_lag 1.00001 1.000012
Rsq(lag_mod)
## [1] 0.9106548
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.2
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0